In the internet age, large amount of textual content is created every minute—by writers, journalists, bloggers, tweeters, social network posters, content makers, or even automatized robots. However, readers prefer to bury themselves in content that is illustrated by an image rather than content comprising merely text.
Users who draft text for various purposes are aware of this psychological effect, so most try to find an appropriate image which fits the content by doing image search using search engines. Many search engines give good, relevant results when the users use appropriate keywords. These engines have built large databases with tagged to describe the content of images in digital archives. Tagging the images, is performed either automatically, or by using advanced image analysis and/or large amount of manual work. Technologies are also available to extract important keywords from text, however, simply combining keyword extraction and image searching does not solve a greater problem. Although this method may render a result for an image for a user to use to illustrate a given text, many issues still need to be solved.
First, a given text may bear more meaning than merely its words. So when it comes to an abstract, multi-layered meaning, keywords may be misleading. Second, the process of choosing an image to illustrate content is more than a simple mechanical process based on keywords. It is a complex process which involves personality, feelings, emotions, and mood. In addition, the same text may convey a multitude of emotional states. For example, the text may contain both sad and happy parts. Some people emphasize sad parts, others prefer to highlight happy parts, others visualize a hybrid mixture of the two emotional states. Because of this, different people oftentimes choose different images for illustrating the very same text.
There is a need for a solution which can learn and model human decision making with respect to the association of images to text and provide human-like, personalized image recommendations to correspond with a given text.